fermi

Calibrated probability distributions from natural-language questions via REST and MCP.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
74%
Qualität der Benennung
80%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,368Tokens (Tool-Definitionen)
~2.1 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.07% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "fermi": {
      "url": "https://fermi.krobar.ai/mcp/sse/"
    }
  }
}

Remote-Endpunkte

https://fermi.krobar.ai/mcp/sse/sse

Was es kann

Tool-Inventar

Tools (3)

🟢 Nur lesen🟡 Schreiben🔴 Löschen⚪ Unbekannt
🟡estimate(question, unit, target_horizon, disclaimer_acknowledged, requested_tier_id, ...)

Return a calibrated probability distribution for a natural-language question. Args: question: The natural-language question (e.g. "What will the price of Bitcoin be in 6 months?"). unit: The unit the answer should be expressed in (e.g. "USD", "people", "meters"). target_horizon: When the estimate applies (e.g. "6 months", "2030", "current"). disclaimer_acknowledged: Must be true. Confirms you understand estimates are probabilistic decision-support only, not financial/medical/legal advice. requested_tier_id: Tier (1=fast sync, 2=grounded sync, 3=deep research async). Default 1. context: Optional extra context to condition the estimate. coverage_probability: Width of the confidence interval (0, 1). Default 0.9 (90% CI). source_inputs: Optional list of structured inputs (files, tables) for the model to consider. Each item needs source_type ("qualitative_text", "spreadsheet", or "structured_file") and either content (text) or structured_data (dict/list). callback_url: Optional webhook URL. When set, the completed or failed result is POSTed here with an HMAC signature, so you don't need to poll for async tiers. api_key: Your Fermi API key from POST /api/v1/accounts. Omit to use the anonymous tier. provider: Optional LLM provider selector ("openai" or "anthropic"). When omitted the server uses its configured default. Tier 1 supports both providers; tiers 2-3 currently accept "openai" only. model: Optional model id for the selected provider. Must match the model configured for (tier_id, provider); omit to accept the default. Returns: EstimateSuccessResponse as a dict: estimation_interval, distribution_family, distribution_parameters, point_estimate, reasoning_summary, assumptions, credits_charged, disclaimer, provider, and model. Raises: ValueError: if the estimate service rejects the request (bad tier, out of credits, unknown provider, missing disclaimer acknowledgement).

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "question": {
      "title": "Question",
      "type": "string"
    },
    "unit": {
      "title": "Unit",
      "type": "string"
    },
    "target_horizon": {
      "title": "Target Horizon",
      "type": "string"
    },
    "disclaimer_acknowledged": {
      "title": "Disclaimer Acknowledged",
      "type": "boolean"
    },
    "requested_tier_id": {
      "default": 1,
      "title": "Requested Tier Id",
      "type": "integer"
    },
    "context": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Context"
    },
    "coverage_probability": {
      "default": 0.9,
      "title": "Coverage Probability",
      "type": "number"
    },
    "source_inputs": {
      "anyOf": [
        {
          "items": {
            "additionalProperties": true,
            "type": "object"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Source Inputs"
    },
    "callback_url": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Callback Url"
    },
    "api_key": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Api Key"
    },
    "provider": {
      "anyOf": [
        {
          "enum": [
            "openai",
            "anthropic"
          ],
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Provider"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Model"
    }
  },
  "required": [
    "question",
    "unit",
    "target_horizon",
    "disclaimer_acknowledged"
  ],
  "title": "estimateArguments"
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": true,
  "title": "estimateDictOutput"
}
🟡feedback(feedback_type, message, sender_email, sender_name, api_key)

Submit feedback, a feature request, or a bug report to the Fermi team. An email is sent to the team and a copy is sent to the address you provide. Args: feedback_type: One of "feedback", "feature_request", or "bug". message: Your feedback or request (10-5000 characters). Be specific. sender_email: Your email address. A confirmation copy is sent here. sender_name: Your name (optional). api_key: Your Fermi API key (optional). If provided, your account ID is attached to the message for context. Returns: Confirmation with status "sent". Raises: ValueError: if validation fails or email delivery fails.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "feedback_type": {
      "enum": [
        "feedback",
        "feature_request",
        "bug"
      ],
      "title": "Feedback Type",
      "type": "string"
    },
    "message": {
      "title": "Message",
      "type": "string"
    },
    "sender_email": {
      "title": "Sender Email",
      "type": "string"
    },
    "sender_name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Sender Name"
    },
    "api_key": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Api Key"
    }
  },
  "required": [
    "feedback_type",
    "message",
    "sender_email"
  ],
  "title": "feedbackArguments"
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "feedbackDictOutput"
}
🟡buy_credits(credits, api_key)

Purchase credits using a saved payment method (off-session). Requires a prior Stripe Checkout session to have saved a card. Credits are $0.50 each. Use GET /api/v1/billing/pricing to confirm the current price. Args: credits: Number of credits to purchase (minimum 1). api_key: Your Fermi API key from POST /api/v1/accounts. Required. Returns: Dict with credits_purchased, new_balance, and payment_status. Raises: ValueError: if authentication fails, no payment method is on file, or the card is declined.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "credits": {
      "title": "Credits",
      "type": "integer"
    },
    "api_key": {
      "title": "Api Key",
      "type": "string"
    }
  },
  "required": [
    "credits",
    "api_key"
  ],
  "title": "buy_creditsArguments"
}

Ausgabe-Schema

{
  "type": "object",
  "additionalProperties": {
    "anyOf": [
      {
        "type": "integer"
      },
      {
        "type": "string"
      }
    ]
  },
  "title": "buy_creditsDictOutput"
}

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